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Record W2177542683 · doi:10.1121/1.4933552

Change in Canada’s national building code—Assessing flanking sound transmission in steel-framed constructions

2015· article· en· W2177542683 on OpenAlexaffabout
Christoph Hoeller, Berndt Zeitler

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2015
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsFlanking maneuverSoundproofingSound transmission classPresentation (obstetrics)Sound (geography)Code (set theory)Transmission (telecommunications)Computer scienceResearch councilClass (philosophy)Architectural engineeringTelecommunicationsAcousticsEngineeringCivil engineeringLinguisticsStructural engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The proposed 2015 edition of the National Building Code of Canada sees a major change in sound insulation requirements. Instead of prescribing requirements for the separating assembly only (in terms of STC values), the Code now sets requirements for the sound insulation performance of the complete system (in terms of Apparent Sound Transmission Class (ASTC) values), including flanking sound transmission. The National Research Council Canada is actively supporting the change in the Code by conducting various projects with industry associations from different construction sectors, in order to provide tools, guidance, and the necessary data for compliance. This presentation focuses on an ongoing joint project between the National Research Council Canada and the Canadian Sheet Steel Building Institute. In the project, the direct and flanking sound transmission in steel-framed assemblies are being investigated. In the presentation, an overview of the project is given, and updates on the current status of the investigation are provided, including measured data concerning the flanking sound transmission in steel-framed constructions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.518
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.299
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2015
Admission routes2
Has abstractyes

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